I am trying to convert a Python script to Julia, using the package `Images`

. To compute the Gaussian filtered gradient of images, the python scipy use `scipy.ndimage.gaussian_filter(img, σ, order=(1,0))`

and `scipy.ndimage.gaussian_filter(img, σ, order=(0,1))`

to get each component of the filtered gradient.

See scipy.ndimage.gaussian_filter for the doc.

See Gradient of a Signal for more about derivatives of Gaussian.

I’ve checked the methods `ImageFiltering.imfilter`

, `ImageFiltering.imgradients`

and the kernels `ImageFiltering.Kernel.gaussian`

, `ImageFiltering.Kernel.DoG`

in the doc.

However, `ImageFiltering.Kernel.DoG`

is not a kernel for derivative of gaussian, but for difference of two gaussian filters. `ImageFiltering.imgradients`

seems not to work with `KernelFactors.gaussian`

.

Is there an equivalent method for this? Or should I find some workaround.